class_docstr: Module that approximates the Manhattan (city block) distance transform of images using convolutions.
  
  Args:
      kernel_size: size of the convolution kernel.
      h: value that influence the approximation of the min function.
code: def __init__(self, kernel_size: int = 3, h: float = 0.35) -> None:
      super().__init__()
      self.kernel_size = kernel_size
      self.h = h
  
file_path: kornia/kornia/contrib/distance_transform.py
imports: import math
  import torch
  from torch import nn
  from kornia.filters import filter2d
  from kornia.utils import create_meshgrid
label_desc: Use image node_id 1 to create a new DistanceTransform object from inherited base classes: nn with example: obj = DistanceTransform(kernel_size, h)